ServiceNow’s AI control tower offers hazy view of spend

IT budgeting has gotten a lot trickier as vendors begin to adjust their pricing to include variable charges for agentic AI usage in addition to seat-based licensing fees.

A case in point is the licensing model for ServiceNow, which has introduced usage-based pricing for its AI components. As part of its AI transformation, the company last week announced Action Fabric, part of its AI Control Tower, which meters agent usage measured in “assists” and charges for it on top of customers’ subscription fees.

Customers receive a set number of assists with their subscription and can choose to pay for more if they need them, said ServiceNow COO Amit Zavery.

The number of assists used in any given agentic interaction varies, however, making it difficult for users to predict their usage, and therefore their bills.

This model is no surprise to industry watchers. “As AI agents begin to move into the heart of the enterprise, we’re moving beyond the age of SaaS into a far more granular era where every last detail of a given workload is monitored, metered, and monetized,” said Carmi Levy, independent technology analyst.

This may not sit well with organizations. “For CIOs and other senior leaders making the strategic decisions and approving the budget, the prospect of paying additional usage-based fees on top of existing subscriptions, and then having to closely monitor usage and spend as AI adoption spreads across the organization, could be an additional irritant as organizations as a whole look for ways to trim spending,” he said. 

ServiceNow’s model is similar to those introduced by SAP and Salesforce, said Info-Tech Research Group advisory fellow Scott Bickley: a base license with entitlement to some level of usage, then consumption-based charging at the data layer and at the AI layer. “With ServiceNow, they’re going to now break out levels of AI capabilities, and each tier will encompass a certain level of functionality, and they each have different levels of capabilities attached to them, and I’m sure different pricing attached to them.”

This will not make CFOs happy, he said; they don’t like variability in pricing, nor unpredictable expenses in the monthly budget. And there’s the potential for things to go expensively wrong if, for example, an autonomous agent goes into a retry loop when its first attempt to perform a task fails, gobbling assist credits with each iteration.

Adoption of the service, he said, will depend on “the ability to decipher the pricing model and match it to business use cases, so that you can accurately predict usage based on what you want to accomplish as an outcome.” If enterprises can’t predict costs and maintain budget versus value over time, they won’t stick with it, he said.

This means that customers now need to pay particular attention to their contracts and find that “line of demarcation where the meter starts running,” Bickley said, noting that historically, ServiceNow’s licensing metrics have lacked clarity on what falls under eligible objects or how things are metered.

“If you can’t manage and predict and build the capabilities to see through what these consumption metrics are going to cost you, then you need to have very flexible contract commercial terms that can help buffer the unanticipated upticks in usage,” he said. “But you really need to have a combination of FinOps maturity, the ability to build clarity in your use cases for AI, the ability to then estimate consumption against those use cases, and a very detailed understanding of the license or subscription metrics.”

Now it has become imperative for customers to take the time to extract those details from ServiceNow, appending them to the contract in a way that’s understandable, he advised. “And if you can’t get them to that point, then you should not be signing the contract.”

Bickley said he feels some sympathy for ServiceNow: “They’re dealing with a moving target themselves. They have to pay for this consumption to the LLM providers as well, but they need to do better in terms of simplifying it for their customers.”